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Large sample size, significance level, and the effect size: Solutions to perils of using big data for academic research

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  • Khalilzadeh, Jalayer
  • Tasci, Asli D.A.

Abstract

The increasing availability of and attention to big data accumulated on different aspects of demand and supply side of industries has resulted in utilization of large samples for academic publications as well. Using large samples, however, creates the issue of guaranteed statistical significance and thus demands reporting the practical significance by using effect size measures. This manuscript is a guide to inform tourism and hospitality academia of the effect size measures for the most commonly used statistical tests.

Suggested Citation

  • Khalilzadeh, Jalayer & Tasci, Asli D.A., 2017. "Large sample size, significance level, and the effect size: Solutions to perils of using big data for academic research," Tourism Management, Elsevier, vol. 62(C), pages 89-96.
    • Handle: RePEc:eee:touman:v:62:y:2017:i:c:p:89-96
    DOI: 10.1016/j.tourman.2017.03.026
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    1. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.
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